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https://blogs.nvidia.com/blog/2020/01/31/ai-galaxies/
The gallery of galaxy images astronomers produce is multiplying faster than the number of selfies on a teen’s new smartphone.
Millions of these images have already been collected by astronomy surveys. But the volume is spiraling with projects like the recent Dark Energy Survey and upcoming Legacy Survey of Space and Time, which will capture billions more.
Volunteers flocked to a recent crowdsource project, Galaxy Zoo, to help classify over a million galaxy images from the Sloan Digital Sky Survey. But citizen science can carry astrophysics only so far.
“Galaxy Zoo was a very successful endeavor, but the rate at which next-generation surveys will gather data will make crowdsourcing methods no longer scalable,” said Asad Khan, a physics doctoral student at the University of Illinois at Urbana-Champaign. “This is where human-in-the-loop techniques present an approach to guide AI to data-driven discovery, including image classification.”
Using transfer learning from the popular image classification model Xception, Khan and his fellow researchers developed a neural network that categorizes galaxy images as elliptical or spiral with expert-level accuracy. Classifying galaxy shapes helps scientists determine how old they are. It can also help them understand more complex questions about dark energy and how fast the universe is expanding.
Automating elements of galaxy classification enables astrophysicists to spend less time on basic labeling and focus on more complex research questions.
The research — the first application of deep transfer learning for galaxy classification — was one of six projects featured at the Scientific Visualization and Data Analytics Showcase at SC19, the annual supercomputing trade show.
The gallery of galaxy images astronomers produce is multiplying faster than the number of selfies on a teen’s new smartphone.
Millions of these images have already been collected by astronomy surveys. But the volume is spiraling with projects like the recent Dark Energy Survey and upcoming Legacy Survey of Space and Time, which will capture billions more.
Volunteers flocked to a recent crowdsource project, Galaxy Zoo, to help classify over a million galaxy images from the Sloan Digital Sky Survey. But citizen science can carry astrophysics only so far.
“Galaxy Zoo was a very successful endeavor, but the rate at which next-generation surveys will gather data will make crowdsourcing methods no longer scalable,” said Asad Khan, a physics doctoral student at the University of Illinois at Urbana-Champaign. “This is where human-in-the-loop techniques present an approach to guide AI to data-driven discovery, including image classification.”
Using transfer learning from the popular image classification model Xception, Khan and his fellow researchers developed a neural network that categorizes galaxy images as elliptical or spiral with expert-level accuracy. Classifying galaxy shapes helps scientists determine how old they are. It can also help them understand more complex questions about dark energy and how fast the universe is expanding.
Automating elements of galaxy classification enables astrophysicists to spend less time on basic labeling and focus on more complex research questions.
The research — the first application of deep transfer learning for galaxy classification — was one of six projects featured at the Scientific Visualization and Data Analytics Showcase at SC19, the annual supercomputing trade show.